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2014年肝脏超声跟踪基准。

The 2014 liver ultrasound tracking benchmark.

作者信息

De Luca V, Benz T, Kondo S, König L, Lübke D, Rothlübbers S, Somphone O, Allaire S, Lediju Bell M A, Chung D Y F, Cifor A, Grozea C, Günther M, Jenne J, Kipshagen T, Kowarschik M, Navab N, Rühaak J, Schwaab J, Tanner C

机构信息

Computer Vision Lab, ETH Zurich, 8092 Zurich, Switzerland.

出版信息

Phys Med Biol. 2015 Jul 21;60(14):5571-99. doi: 10.1088/0031-9155/60/14/5571. Epub 2015 Jul 2.

Abstract

The Challenge on Liver Ultrasound Tracking (CLUST) was held in conjunction with the MICCAI 2014 conference to enable direct comparison of tracking methods for this application. This paper reports the outcome of this challenge, including setup, methods, results and experiences. The database included 54 2D and 3D sequences of the liver of healthy volunteers and tumor patients under free breathing. Participants had to provide the tracking results of 90% of the data (test set) for pre-defined point-landmarks (healthy volunteers) or for tumor segmentations (patient data). In this paper we compare the best six methods which participated in the challenge. Quantitative evaluation was performed by the organizers with respect to manual annotations. Results of all methods showed a mean tracking error ranging between 1.4 mm and 2.1 mm for 2D points, and between 2.6 mm and 4.6 mm for 3D points. Fusing all automatic results by considering the median tracking results, improved the mean error to 1.2 mm (2D) and 2.5 mm (3D). For all methods, the performance is still not comparable to human inter-rater variability, with a mean tracking error of 0.5-0.6 mm (2D) and 1.2-1.8 mm (3D). The segmentation task was fulfilled only by one participant, resulting in a Dice coefficient ranging from 76.7% to 92.3%. The CLUST database continues to be available and the online leader-board will be updated as an ongoing challenge.

摘要

肝脏超声跟踪挑战赛(CLUST)与2014年医学图像计算与计算机辅助干预国际会议(MICCAI 2014)同期举办,旨在对该应用的跟踪方法进行直接比较。本文报告了此次挑战赛的结果,包括设置、方法、结果和经验。数据库包含54个健康志愿者和肿瘤患者在自由呼吸状态下肝脏的二维和三维序列。参与者必须针对预定义的点地标(健康志愿者)或肿瘤分割(患者数据)提供90%数据(测试集)的跟踪结果。在本文中,我们比较了参与挑战赛的六种最佳方法。组织者根据手动标注进行了定量评估。所有方法的结果显示,二维点的平均跟踪误差在1.4毫米至2.1毫米之间,三维点的平均跟踪误差在2.6毫米至4.6毫米之间。通过考虑中位数跟踪结果融合所有自动结果,可将平均误差分别提高到1.2毫米(二维)和2.5毫米(三维)。对于所有方法,其性能仍无法与人类评分者之间的变异性相媲美,二维平均跟踪误差为0.5 - 0.6毫米,三维为1.2 - 1.8毫米。分割任务只有一名参与者完成,其Dice系数在76.7%至92.3%之间。CLUST数据库仍然可用,在线排行榜将作为一项持续挑战赛进行更新。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5377/5454593/61c4153ce57f/pmb515767f01_hr.jpg

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